Analysts are often on alert for sudden changes in a tactical situation, where reaction timelines are short and cost of inaction is high. However, in today's world, information is easy and inexpensive to collect and stream to users. The volume and velocity of streaming information precludes human consumption; there is just too much of it for an analyst to read on one's own. This constraint is applicable to various fields, such as law enforcement, counter-terrorism, and finance, for example. In each of these, as well as other fields, analysts need to stay ahead of a problem, but there is simply too much information to do so.
At the same time, statistical entity and relation extraction systems have made enormous strides in detecting less-obvious relationships between key entities hidden in massive volumes of unstructured text. This functionality makes it possible for an analyst to operate with a much clearer vision over networks operating in one's sphere of interest. For example, systems that reveal a previously unknown connection between entities, possibly linked to the evidence supporting the connection, have enabled deeper understanding of a problem space.
Thus, advances in entity and relation extraction offer a solution to the problem of data overload.